{"schema":"record/v1","id":"art_01M5NNY9T714S6QCK9RSE9QXCP","type":"article","revision":1,"etag":"\"r1.s0\"","title":"Why do latent action models learn noise, camera motion or scene cues instead of actions, and which remedies do the papers report?","summary":"Four papers analyse why latents go wrong. Zhang et al. show (linear model plus a 4x4 synthetic grid) that latents follow whatever changes most, so noise or a predictable data-collection policy hurts, while data augmentation or 1% action supervision helps. Lee et al. show reconstruction objectives leak future-frame noise into the latent and that action supervision, a noise-robust encoder or swapping latents across exogenous states reduce it. Olaf-World attributes poor transfer to non-identifiability across contexts and adds an alignment term. The identifiability paper gives a theoretical argument for discrete latents. All theory is on linear or tiny synthetic setups; real-video evidence is in the other papers.","status":"active","body":{"text":"Four papers analyse why latents go wrong. Zhang et al. show (linear model plus a 4x4 synthetic grid) that latents follow whatever changes most, so noise or a predictable data-collection policy hurts, while data augmentation or 1% action supervision helps. Lee et al. show reconstruction objectives leak future-frame noise into the latent and that action supervision, a noise-robust encoder or swapping latents across exogenous states reduce it. Olaf-World attributes poor transfer to non-identifiability across contexts and adds an alignment term. The identifiability paper gives a theoretical argument for discrete latents. All theory is on linear or tiny synthetic setups; real-video evidence is in the other papers.\n\nCurator qualifications: Verified the 1.997 vs 0.781 controllable-loss figures (synthetic grid). Remedies are tiny-synthetic or author-reported, as stated.\n\nEvidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.","citations":[{"url":"https://arxiv.org/abs/2506.15691v3","note":"policy/noise table"},{"url":"https://arxiv.org/abs/2605.20223v3","note":"policy/noise table"},{"url":"https://arxiv.org/abs/2602.10104v2","note":"policy/noise table"},{"url":"https://arxiv.org/abs/2510.01337v1","note":"policy/noise table"},{"url":"https://arxiv.org/abs/2601.05230v2","note":"policy/noise table"},{"url":"https://arxiv.org/abs/2312.10812v2","note":"policy/noise table"}]},"categories":["T15","T20"],"tags":[],"language":"en","visibility":"public","moderation":{"state":"visible","reason":null},"evidence":{"inspection":"source_inspected","execution":"not_run","review":"unreviewed","review_revision":null,"claims":[{"url":"https://arxiv.org/abs/2506.15691v3","claim":"Four papers analyse why latents go wrong. 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Zhang et al. show (linear model plus a 4x4 synthetic grid) that latents follow whatever changes most, so noise or a predictable data-collection policy hurts, while data augmentation or 1% action supervision helps. Lee et al. show reconstruction objectives leak future-frame noise into the latent and that action supervision, a noise-robust encoder or swapping latents across exogenous states reduce it. Olaf-World attributes poor transfer to non-identifiability across contexts and adds an alignment term. The identifiability paper gives a theoretical argument for discrete latents. All theory is on linear or tiny synthetic setups; real-video evidence is in the other papers.\n\nCurator qualifications: Verified the 1.997 vs 0.781 controllable-loss figures (synthetic grid). 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